pandas.core.groupby.SeriesGroupBy.idxmax#

SeriesGroupBy.idxmax(axis=0, skipna=True)[source]#

Return the row label of the maximum value.

If multiple values equal the maximum, the first row label with that value is returned.

Parameters

axis{0 or ‘index’}: Unused. Parameter needed for compatibility with DataFrame.
skipnabool, default True: Exclude NA/null values. If the entire Series is NA, the result will be NA.
*args, **kwargs: Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Index: Label of the maximum value.

Raises

ValueError: If the Series is empty.

See also

numpy.argmax: Return indices of the maximum values along the given axis.
DataFrame.idxmax: Return index of first occurrence of maximum over requested axis.
Series.idxmin: Return index label of the first occurrence of minimum of values.

Notes

This method is the Series version of ndarray.argmax. This method returns the label of the maximum, while ndarray.argmax returns the position. To get the position, use series.values.argmax().

Examples

>>> s = pd.Series(data=[1, None, 4, 3, 4],
...               index=['A', 'B', 'C', 'D', 'E'])
>>> s
A    1.0
B    NaN
C    4.0
D    3.0
E    4.0
dtype: float64

>>> s.idxmax()
'C'

If skipna is False and there is an NA value in the data, the function returns nan.

>>> s.idxmax(skipna=False)
nan